1. Field of the Invention
Embodiments of this invention are generally related to data analysis, and to recognizing patterns in large data sets representing networks of entities such as people, places or things.
2. Description of the Prior Art
Network analysis is a growing field across many domains, including computer vision, social media marketing, transportation networks, and intelligence analysis. The growing use of digital communication devices and platforms, as well as persistent surveillance sensors, has resulted in explosion of the quantity of data and stretched the abilities of current technologies to process this data and draw meaningful conclusions.
Elucidation of network structural organization, connectedness, and relevance in complex environments, such as counter-insurgency operating environments, represents a challenge for social network analysis. This is due to a complex convolution of the evolving size and nature of individual networks operating over widely-varying geographic extents, with differing intent and visibility, and with varying degrees of overlap between network types. These characteristics result in practical issues, namely highly-fragmented, uncertain, noisy data coupled with significant time-dependent behavioral changes. Because of the complex nature of counter-insurgency operations, it is not just a matter of identifying stable networks of well-defined “bad guys.” It is necessary to understand the complex social networks within which shifting allegiances create an ever-changing threat. Constant monitoring and reasoning about the status of adversarial activities and relationships is mandatory for accurate situational awareness. Thus there is a need for accurate, effective, and generalizable tools for rapidly revealing and analyzing networks of individuals, organizations, activities, places, and resources.
Current technologies fall far short of these goals. For example, social network analysis techniques cannot find relational patterns in data nor filter out irrelevant entities; traditional interaction analysis models cannot remove noise from data or work at different levels of granularity; traditional probabilistic temporal activity tracking and actor profiling cannot find patterns of interdependent multi-type activities performed by multiple actors in parallel in both space and time; and parallel plan recognition models assume that data association of observed entities and their roles is already known, and the signal-to-noise ratio is significant. Yet characteristics that current approaches lack are helpful for achieving robust elucidation of relevant networks for complex environments such as in counter-insurgency operation networks.